Dr Francisco Perez-Reche

Dr Francisco Perez-Reche

Senior Lecturer

Dr Francisco Perez-Reche
Dr Francisco Perez-Reche

Contact Details

work +44 (0)1224 273418
The University of Aberdeen Institute for Complex Systems and Mathematical Biology School of Natural and Computing Sciences Meston Building. Room 336 University of Aberdeen Aberdeen AB24 3UE, UK
Tel: +44 (0)1224 273418


  • Aug 2017 - present. Senior Lecturer. University of Aberdeen.
  • Sept 2012 - Jul 2017. Lecturer. University of Aberdeen.
  • 2011 - Aug 2012. Lecturer. University of Abertay Dundee.
  • 2008 - 2010. Research associate. University of Cambridge.
  • 2006 - 2008. Marie Curie experienced researcher sharing time between Ecole Polytechnique (Paris) and Università di Padova.
  • 2007 - 2008. Postdoctoral Scholar at Université Pierre et Marie Curie.
  • 2005. Ph.D. in Physics. Universitat de Barcelona.

Research Interests

My research has a marked interdisciplinary character which, broadly speaking, uses mathematical modelling and statistical mechanics as core disciplines to address questions relevant to a broad range of fields including condensed matter physics, materials science, biological and environmental sciences.

Current Research

Spread and evolution of pathogens

We integrate statistical analysis, mathematical models, genomics and risk assessment to understand the transmission dynamics of bacterial gastrointestinal pathogens such as Campylobacter or Listeria. These pathogens can be found in the environment and are hosted by different types of hosts including humans or animals used for food production (e.g. chickens or salmon).

Dynamical processes on complex networks

In the last decades, network science has emerged as a successful field aiming at a systematic understanding of many complex systems. We use network representations of populations of individuals to describe the spread of infection or social phenomena over a wide range of spatial and temporal scales (e.g. from towns and farms to entire countries or the whole world). The main aim is to understand how the mechanisms of transmission of, e.g. infection or ideas, between individuals affect the overall ability of the spreading phenomenon to invade a large portion of the population. This understanding is crucial to optimise the spread of beneficial processes (e.g. good behaviour or news) or prevent spread of damaging processes (e.g. infections or misinformation)SynergyInvasions of constructive and interferring spreaders

Soil networkSoil biological invasion using network representations for the soil pore space

Explosive contagion

Explosive transitions to large social contagion 


Explosive immunisation 

 Explosive immunisation to prevent epidemics by vaccinating as few nodes as possible


Maximum size of epidemics as a function of the fraction q of immunised nodes in (a) the Albert-Barabasi model, (b) the configuration model, (c) cattle transportation network in Scotland and (d)  airport transportation networks. Different curves correspond to different immunisation strategies.


Structural phase transitions

Many solids undergo complex structural changes when subject to changes of temperature or mechanical load. For instance, this is the case of shape-memory alloys used for medical implants, cars, and many other applications. Such structural changes are associated with phase transitions that typically obey intermittent dynamics characterised by discrete transformation events and intrinsic evolving heterogeneity (e.g. dislocations). Our research uses lattice models derived from continuous theories of mechanics of solids to understand these complex dynamics.

Cycled martensiteNumerical simulations predicting the evolution of the microstructure of martensites under thermal cycling


Avalanches in disordered systems

Systems exhibiting a collective avalanche response when driven externally are ubiquitous. Examples of such phenomena include earthquakes, magnetization reversal, structural phase transitions and collective opinion shifts. The zero-temperature Random Field Ising Model (zt-RFIM) is a prototype model for this class of systems assuming that avalanche behaviour is a consequence of disorder (i.e. heterogeneities of diverse nature) present in the system. Our results are based on exact analytical approaches and numerical simulations.

RFIM bilayer Bethe latticeExact results for the magnetisation and correlation length of the zt-RFIM on bi-layered Bethe lattices


Capillary condensation in porous media

Physical systems which consist of networks of pores, such as Vycor, Silica aerogels, porous rocks, soil, and others, have a wide spectrum of applications, ranging from molecular filters and catalysts to fuel storage. Capillary condensation is an important and peculiar physical phenomenon occurring in many such systems. We devise lattice gas models which allow the heterogeneity of porous media to be properly incorporated in the description condensation. 

SBA-15 porous networkLattice gas model to study capillary condensation in mesoporous silica SBA-15


Colloidal suspensions - Transport in porous media

Colloids play an important role in the transfer of nutrients and pollutants in the environment. We are combining mathematical models and experiments to understand the dynamics of colloidal suspensions in solid surfaces and porous media.  

Stain evaporating dropletMorphologies of stains left by evaporating drops. Different morphologies and attachments are obtained depending on the surface tension 


Biochar reverse engineering

Biochar are widely used materials for soil management. The central objective of this research is to determine how the physico-chemical properties of biochar are influenced by the processing parameters (i.e. the starting organic material and pyrolysis temperature). A Biochar Engineering web application was developed to determine suitable preparation strategies to obtain biochars with pre-set properties.

Biochar networksCorrelations between different properties of biochar



Teaching Responsibilities

PX1016: Understanding the Physical World

PX1019 - Rainbows


ST1506: Understanding data

Monty Hall


 PX2512: Cosmology, Astronomy and Modern Physics



PX5009: Machine learning

 KNN method


[In previous years...] MX3023: Mechanics A


Further Info

Admin Responsibilities

Director of Undergraduate Pathways in Physics